https://github.com/d0r1h/ML-University
Machine Learning Open Source University
https://github.com/d0r1h/ML-University
artificial-intelligence awsome awsome-list computer-science course data-science deep-learning free learning machine-learning mathematics natural-language-processing neural-network open-source reinforcement-learning university
Last synced: 5 days ago
JSON representation
Machine Learning Open Source University
- Host: GitHub
- URL: https://github.com/d0r1h/ML-University
- Owner: d0r1h
- Created: 2020-08-29T15:40:50.000Z (over 4 years ago)
- Default Branch: master
- Last Pushed: 2025-01-13T13:04:02.000Z (4 months ago)
- Last Synced: 2025-01-13T13:51:17.906Z (4 months ago)
- Topics: artificial-intelligence, awsome, awsome-list, computer-science, course, data-science, deep-learning, free, learning, machine-learning, mathematics, natural-language-processing, neural-network, open-source, reinforcement-learning, university
- Homepage:
- Size: 844 KB
- Stars: 871
- Watchers: 36
- Forks: 110
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
Awesome Lists containing this project
README
A Free Machine Learning University
Machine Learning Open Source University is an IDEA of free-learning of a ML enthusiast for all other ML enthusiast
**This list is continuously updated** - And if you are a Ml practitioner and have some good suggestions to improve this or have somegood resources to share, you create pull request and contribute.
**Table of Contents**
1. [Getting Started](#getting-started)
2. [Mathematics](#mathematics)
3. [Machine Learning](#machine-learning)
4. [Deep Learning](#deep-learning)
5. [Natural language processing](#natural-language-processing)
6. [Reinforcement learning](#reinforcement-learning)
7. [LLM](LLM (Large Language Model))
8. [Books](#books)
9. [ML in Production](#ml-in-production)
10. [Quantum ML](#quantum-ml)
11. [DataSets](#datasets)
12. [Other Useful Websites](#other-useful-websites)
13. [Other Useful GitRrpo](#other-useful-gitrepo)
14. [Blogs and Webinar](#blogs-and-webinar)
15. [Must Read Research Paper](#must-read-research-paper)
16. [Company Tech Blogs](#company-tech-blogs)
## Getting Started
| Title and Source | Link |
|------------------------------------------------------------ | -------------------------------------------------------------|
| Elements of AI : Part-1 | [WebSite](https://course.elementsofai.com/) |
| Elements of AI : Part-2 | [WebSite](https://buildingai.elementsofai.com/) |
| CS50’s Introduction to AI **Harvard** | [Cs50 WebSite](https://cs50.harvard.edu/ai/2020/) |
| Intro to Computational Thinking and Data Science **MIT** | [WebSite](https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-0002-introduction-to-computational-thinking-and-data-science-fall-2016/)
| Practical Data Ethics | [fast.ai](https://ethics.fast.ai/)
| Machine learning Mastery Getting Started | [machinelearningmastery](https://machinelearningmastery.com/start-here/)
| Design and Analysis of Algorithms **MIT** | [ocw.mit.edu](https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-046j-design-and-analysis-of-algorithms-spring-2015/)
| AI: Principles and Techniques **Stanford** | [YouTube](https://www.youtube.com/playlist?list=PLoROMvodv4rO1NB9TD4iUZ3qghGEGtqNX)|
| The Private AI Series | [openmined](https://courses.openmined.org/courses)|
## Mathematics
| Title and Source | Link |
|------------------------------------------------------------ | -------------------------------------------------------------
| Statistics in Machine Learning (Krish Naik) | [YouTube](https://www.youtube.com/playlist?list=PLZoTAELRMXVMhVyr3Ri9IQ-t5QPBtxzJO)
| Computational Linear Algebra for Coders | [fast.ai](https://github.com/fastai/numerical-linear-algebra/blob/master/README.md)
| Linear Algebra **MIT** | [WebSite](https://openlearninglibrary.mit.edu/courses/course-v1:OCW+18.06SC+2T2019/course/)|
| Statistics by zstatistics | [WebSite](https://www.zstatistics.com/videos)|
| Essence of linear algebra by 3Blue1Brown | [YouTube](https://www.youtube.com/playlist?list=PLZHQObOWTQDPD3MizzM2xVFitgF8hE_ab)|
| SEEING THEORY (Visual Probability) **brown** | [WebSite](https://seeing-theory.brown.edu/basic-probability/index.html)|
| Matrix Methods in Data Analysis,and Machine Learning **MIT** | [WebSite](https://ocw.mit.edu/courses/mathematics/18-065-matrix-methods-in-data-analysis-signal-processing-and-machine-learning-spring-2018/)
| Math for Machine Learning | [YouTube](https://www.youtube.com/playlist?app=desktop&list=PLD80i8An1OEGZ2tYimemzwC3xqkU0jKUg) |
| Statistics for Applications **MIT** | [YouTube](https://www.youtube.com/playlist?list=PLUl4u3cNGP60uVBMaoNERc6knT_MgPKS0)
| Introduction to Mathematical Thinking | [Website](http://imt-decal.org/)|
## Machine Learning
| Title and Source | Link |
|------------------------------------------------------------ | -------------------------------------------------------------|
| Introduction to Machine Learning with scikit-learn | [dataschool](https://courses.dataschool.io/introduction-to-machine-learning-with-scikit-learn)|
| Introduction to Machine Learning | [sebastianraschka](https://sebastianraschka.com/blog/2021/ml-course.html)
| Open Machine Learning Course | [mlcourse.ai](https://mlcourse.ai/) |
| Machine Learning (CS229) **Stanford** | [WebSite](http://cs229.stanford.edu/syllabus-spring2020.html) [YouTube](https://www.youtube.com/playlist?list=PLoROMvodv4rMiGQp3WXShtMGgzqpfVfbU)|
| Introduction to Machine Learning **MIT** | [WebSite](https://tinyurl.com/ybl6udcr) |
| Machine Learning Systems Design 2021 (CS329S) **Stanford** | [WebSite](https://stanford-cs329s.github.io/syllabus.html) |
| Applied Machine Learning 2020 (CS5787) **Cornell Tech** | [YouTube](https://www.youtube.com/playlist?list=PL2UML_KCiC0UlY7iCQDSiGDMovaupqc83)
| Machine Learning for Healthcare **MIT** | [WebSite](https://tinyurl.com/yxgeesdf) |
| Machine Learning for Trading **Georgia Tech** | [WebSite](https://lucylabs.gatech.edu/ml4t/) |
| Introduction to Machine Learning for Coders | [fast.ai](https://course18.fast.ai/ml.html)
| Machine Learning Crash Course | [Google AI](https://developers.google.com/machine-learning/crash-course)|
| Machine Learning with Python | [freecodecamp](https://www.freecodecamp.org/learn/machine-learning-with-python/)|
| Deep Reinforcement Learning:CS285 **UC Berkeley** | [YouTube](https://www.youtube.com/playlist?list=PL_iWQOsE6TfURIIhCrlt-wj9ByIVpbfGc)|
| Probabilistic Machine Learning **University of Tübingen** | [YouTube](https://www.youtube.com/playlist?list=PL05umP7R6ij1tHaOFY96m5uX3J21a6yNd)|
| Machine Learning with Graphs(CS224W) **Stanford** | [YouTube](https://www.youtube.com/playlist?list=PLoROMvodv4rPLKxIpqhjhPgdQy7imNkDn)|
| Machine Learning in Production **CMU** | [WebSite](https://ckaestne.github.io/seai/)|
| Machine Learning & Deep Learning Fundamentals | [deeplizard](https://deeplizard.com/learn/video/gZmobeGL0Yg)|
| Interpretability and Explainability in Machine Learning | [WebSite](https://interpretable-ml-class.github.io/)|
| Practical Machine Learning 2021 **Stanford** | [WebSite](https://c.d2l.ai/stanford-cs329p/index.html#)|
| Machine Learning **VU University** | [WebSite](https://mlvu.github.io/)|
| Machine Learning for Cyber Security **Purdue University** | [YouTube](https://www.youtube.com/playlist?list=PL74sw1ohGx7GHqDHCkXZeqMQBVUTMrVLE)|
| Audio Signal Processing for Machine Learning | [YouTube](https://www.youtube.com/playlist?list=PL-wATfeyAMNqIee7cH3q1bh4QJFAaeNv0)|
| Machine learning & causal inference **Stanford** | [YouTube](https://www.youtube.com/playlist?list=PLxq_lXOUlvQAoWZEqhRqHNezS30lI49G-)|
| Machine learning cs156 **caltech** | [YouTube](https://www.youtube.com/playlist?list=PLD63A284B7615313A) |
| Multimodal machine learning (MMML) **CMU** | [WebSite](https://cmu-multicomp-lab.github.io/mmml-course/fall2020/) [YouTube](https://www.youtube.com/playlist?list=PL-Fhd_vrvisNup9YQs_TdLW7DQz-lda0G) |
| Advanced Topics in Machine Learning **Caltech** | [WebSite](https://1five9.github.io/)
## Deep Learning
| Title and Source | Link |
|------------------------------------------------------------ | -------------------------------------------------------------|
| Introduction to Deep Learning(6.S191) **MIT** | [YouTube](https://www.youtube.com/playlist?list=PLtBw6njQRU-rwp5__7C0oIVt26ZgjG9NI) |
| Introduction to Deep Learning | [sebastianraschka](https://sebastianraschka.com/blog/2021/dl-course.html)
| Deep Learning **NYU** | [WebSite](https://atcold.github.io/pytorch-Deep-Learning/) [2021](https://atcold.github.io/NYU-DLSP21/) |
| Deep Learning (CS182) **UC Berkeley** | [YouTube](https://www.youtube.com/playlist?list=PL_iWQOsE6TfVmKkQHucjPAoRtIJYt8a5A)
| Deep Learning Lecture Series **DeepMind x UCL** | [YouTube](https://www.youtube.com/playlist?list=PLqYmG7hTraZCDxZ44o4p3N5Anz3lLRVZF)|
| Deep Learning (CS230) **Stanford** | [WebSite](https://cs230.stanford.edu/lecture/) |
| CNN for Visual Recognition(CS231n) **Stanford** | [WebSite-2020](https://cs231n.github.io/) [YouTube-2017](https://tinyurl.com/y2gghbvs)|
| Full Stack Deep Learning | [WebSite](https://course.fullstackdeeplearning.com/)[2021](https://fullstackdeeplearning.com/spring2021/)|
| Practical Deep Learning for Coders, v3 | [fast.ai](https://course19.fast.ai/index.html) |
| Deep Learning Crash Course 2021 d2l.ai | [YouTube](https://www.youtube.com/playlist?list=PLZSO_6-bSqHQsDaBNtcFwMQuJw_djFnbd)|
| Deep Learning for Computer Vision **Michigan** | [WebSite](https://web.eecs.umich.edu/~justincj/teaching/eecs498/FA2020/)|
| Neural Networks from Scratch in Python by Sentdex | [YouTube](https://www.youtube.com/playlist?app=desktop&list=PLQVvvaa0QuDcjD5BAw2DxE6OF2tius3V3)|
| Keras - Python Deep Learning Neural Network API | [deeplizard](https://deeplizard.com/learn/video/RznKVRTFkBY)|
| Reproducible Deep Learning | [sscardapane.it](https://www.sscardapane.it/teaching/reproducibledl/)|
| PyTorch Fundamentals | [microsoft](https://docs.microsoft.com/en-us/learn/paths/pytorch-fundamentals/)|
| Geometric Deep Learing (GDL100) | [geometricdeeplearning](https://geometricdeeplearning.com/lectures/)|
| Deep learning Neuromatch Academy | [neuromatch](https://deeplearning.neuromatch.io/tutorials/intro.html)
| Deep Learning for Molecules and Materials | [WebSite](https://whitead.github.io/dmol-book/intro.html)|
| Deep Learning course for Vision | [arthurdouillard.com](https://arthurdouillard.com/deepcourse/)|
| Deep Multi-Task and Meta Learning (CS330) **Stanford** | [WebSite](https://cs330.stanford.edu/) [YouTube](https://www.youtube.com/playlist?list=PLoROMvodv4rMC6zfYmnD7UG3LVvwaITY5)|
| Deep Learning Interviews book | [WebSite](https://github.com/BoltzmannEntropy/interviews.ai)|
| Deep Learning for Computer Vision 2021 | [YouTube](https://www.youtube.com/playlist?list=PL_Z2_U9MIJdNgFM7-f2fZ9ZxjVRP_jhJv)
| Deep Learning 2022 **CMU** | [YouTube](https://www.youtube.com/playlist?list=PLp-0K3kfddPxRmjgjm0P1WT6H-gTqE8j9)
| UvA Deep Learning | [WebSite](https://uvadlc.github.io/)
## Natural language processing
| Title and Source | Link |
| ------------------------------------------------------------ | -----------------------------------------------------------|
| Natural Language Processing AWS | [YouTube](https://www.youtube.com/playlist?list=PL8P_Z6C4GcuWfAq8Pt6PBYlck4OprHXsw)
| NLP - Krish Naik | [YouTube](https://www.youtube.com/playlist?list=PLZoTAELRMXVMdJ5sqbCK2LiM0HhQVWNzm)
| NLP with Deep Learning(CS224N) 2019 **Stanford** | [YouTube](https://www.youtube.com/playlist?list=PLoROMvodv4rOhcuXMZkNm7j3fVwBBY42z) [2021](https://www.youtube.com/playlist?list=PLoROMvodv4rOSH4v6133s9LFPRHjEmbmJ)
| A Code-First Introduction to Natural Language Processing | [fast.ai](https://www.fast.ai/2019/07/08/fastai-nlp/)|
| CMU Neural Nets for NLP 2021 **Carnegie Mellon University** | [YouTube](https://www.youtube.com/playlist?list=PL8PYTP1V4I8AkaHEJ7lOOrlex-pcxS-XV)|
| Speech and Language Processing **Stanford** | [WebSite](https://web.stanford.edu/~jurafsky/slp3/) |
| Natural Language Understanding (CS224U) **Stanford** | [YouTube](https://www.youtube.com/playlist?list=PLoROMvodv4rObpMCir6rNNUlFAn56Js20) [2022](https://web.stanford.edu/class/cs224u/)
| NLP with Dan Jurafsky and Chris Manning, 2012 **Stanford** | [YouTube](https://www.youtube.com/playlist?list=PLoROMvodv4rOFZnDyrlW3-nI7tMLtmiJZ)|
| Intro to NLP with spaCy | [YouTube](https://www.youtube.com/playlist?list=PLBmcuObd5An559HbDr_alBnwVsGq-7uTF)|
| Advanced NLP with spaCy | [website](https://course.spacy.io/en/) |
| Applied Language Technology | [website](https://applied-language-technology.readthedocs.io/en/latest/)|
| Advanced Natural Language Processing **Umass** | [website](https://people.cs.umass.edu/~miyyer/cs685/schedule.html) [YouTube 2020](https://www.youtube.com/playlist?list=PLWnsVgP6CzadmQX6qevbar3_vDBioWHJL)|
| Huggingface Course | [huggingface.co](https://huggingface.co/course/chapter1?fw=tf)|
| NLP Course **Michigan** | [github](https://github.com/deskool/nlp-class)|
| Multilingual NLP 2020 **CMU** | [YouTube](https://www.youtube.com/playlist?list=PL8PYTP1V4I8CHhppU6n1Q9-04m96D9gt5)|
| Advanced NLP 2021 **CMU** | [YouTube](https://www.youtube.com/playlist?list=PL8PYTP1V4I8AYSXn_GKVgwXVluCT9chJ6)|
| Transformers United **stanford** | [Website](https://web.stanford.edu/class/cs25/) [YouTube](https://www.youtube.com/playlist?list=PLoROMvodv4rNiJRchCzutFw5ItR_Z27CM) |
| CS324 Large Language Models | [Website](https://stanford-cs324.github.io/winter2022/)|
## Reinforcement learning
| Title and Source | Link |
|------------------------------------------------------------ | -----------------------------------------------------------|
| Reinforcement Learning(CS234) **Stanford** | [YouTube-2019](https://www.youtube.com/playlist?list=PLoROMvodv4rOSOPzutgyCTapiGlY2Nd8u)|
| Introduction to reinforcement learning **DeepMind** | [YouTube-2015](https://www.youtube.com/playlist?list=PLqYmG7hTraZDM-OYHWgPebj2MfCFzFObQ)|
| Reinforcement Learning Course **DeepMind & UCL** | [YouTube-2018](https://www.youtube.com/playlist?list=PLqYmG7hTraZBKeNJ-JE_eyJHZ7XgBoAyb)|
| Advanced Deep Learning & Reinforcement Learning | [YouTube](https://www.youtube.com/playlist?list=PLqYmG7hTraZDNJre23vqCGIVpfZ_K2RZs)|
| DeepMind x UCL Reinforcement Learning 2021 | [YouTube](https://www.youtube.com/playlist?list=PLqYmG7hTraZDVH599EItlEWsUOsJbAodm)
## LLM (Large Language Model)
| Title and Source | Link |
|------------------------------------------------------------ | -----------------------------------------------------------|
| Large Language Model Systems | [Website](https://llmsystem.github.io/llmsystem2025spring/) |
## Books
| Title and Source | Link |
|------------------------------------------------------------ | -----------------------------------------------------------|
| Scientific Python Lectures | [ScipyLectures](https://scipy-lectures.org/_downloads/ScipyLectures-simple.pdf)|
| Mathematics for Machine Learning | [mml-book](https://mml-book.github.io/book/mml-book.pdf) |
| An Introduction to Statistical Learning | [statlearning](https://www.statlearning.com/) |
| Think Stats | [Think Stats](https://greenteapress.com/wp/think-stats-2e/)|
| Python Data Science Handbook | [Python For DS](https://jakevdp.github.io/PythonDataScienceHandbook/)|
| Natural Language Processing with Python - NLTK | [NLTK](https://www.nltk.org/book/) |
| Deep Learning by Ian Goodfellow | [deeplearningbook](https://www.deeplearningbook.org/) |
| Dive into Deep Learning | [d2l.ai](https://d2l.ai/index.html)
| Approaching (Almost) Any Machine Learning Problem | [AAANLP](https://github.com/abhishekkrthakur/approachingalmost/blob/master/AAAMLP.pdf)|
| Neural networks and Deep learning | [neuralnetworksanddeeplearning](http://neuralnetworksanddeeplearning.com/index.html)|
| AutoML: Methods, Systems, Challenges (first book on AutoML) | [automl](https://www.automl.org/book/)|
| Feature Engineering and Selection | [bookdown.org](https://bookdown.org/max/FES/)|
| Introduction to Machine Learning Interviews Book | [huyenchip.com](https://huyenchip.com/ml-interviews-book/)|
| Hands-On Machine Learning with R | [website](https://bradleyboehmke.github.io/HOML/)|
| Zero to Mastery TensorFlow for Deep Learning Book | [dev.mrdbourke.com/](https://dev.mrdbourke.com/tensorflow-deep-learning/)|
| Introduction to Probability for Data Science | [probability4datascience](https://probability4datascience.com/)|
| Graph Representation Learning Book | [cs.mcgill.ca](https://www.cs.mcgill.ca/~wlh/grl_book/)|
| Interpretable Machine Learning | [christophm](https://christophm.github.io/interpretable-ml-book/)|
| Computer Vision: Algorithms and Applications, 2nd ed. | [szeliski.org](https://szeliski.org/Book/)
## ML in Production
| Title and Source | Link |
|------------------------------------------------------------ | -----------------------------------------------------------|
| Introduction to Docker | [Docker](https://carpentries-incubator.github.io/docker-introduction/)|
| MLOps Basics | [GitHub](https://github.com/graviraja/MLOps-Basics)|
| Effective MLOps: Model Development | [wandb](https://www.wandb.courses/courses/effective-mlops-model-development/)|
## Quantum ML
| Title and Source | Link |
|------------------------------------------------------------ | -----------------------------------------------------------|
| Quantum machine learning | [pennylane.ai](https://pennylane.ai/qml/)|
## DataSets
| Title and Source | Link |
|------------------------------------------------------------ | -----------------------------------------------------------|
| Yelp Open Dataset | [yelp](https://www.yelp.com/dataset) |
| Machine Translation | [website](https://www.manythings.org/anki/) |
| IndicNLP Corpora (Indian languages) | [ai4bharat](https://indicnlp.ai4bharat.org/explorer/) |
| Amazon product co-purchasing network metadata | [snap.stanford.edu/](https://snap.stanford.edu/data/amazon-meta.html)|
| Stanford Question Answering Dataset (SQuAD) | [website](https://rajpurkar.github.io/SQuAD-explorer/)
## Other Useful Websites
1. [Papers with Code](https://paperswithcode.com/sota)
2. [Two Minute Papers - Youtube](https://www.youtube.com/c/K%C3%A1rolyZsolnai/videos)
3. [The Missing Semester of Your CS Education](https://missing.csail.mit.edu/2020/)
4. [Workera : Measure data-AI skills](https://workera.ai/)
5. [Machine learning mastery](https://machinelearningmastery.com/start-here/)
6. [From Data to viz: Guide for your graph](https://www.data-to-viz.com/)
7. [datatalks club](https://datatalks.club/)
8. [Machine Learning for Art](https://ml4a.net/fundamentals/)
10. [applyingml](https://applyingml.com/)
11. [Deep Learning Drizzle](https://deep-learning-drizzle.github.io/index.html#opt4ml)
12. [The Machine & Deep Learning Compendium](https://book.mlcompendium.com/)
13. [connectedpapers - Research Papers](https://www.connectedpapers.com/)
14. [Papers and Latest Research - deepai](https://deepai.org/)
15. [Tracking Progress in NLP](https://nlpprogress.com/)
16. [NLP Blogs by Sebastian Ruder](https://www.ruder.io/)
17. [labmlai for papers](https://papers.labml.ai/)
## Other Useful GitRepo
1. [Applied-ml - Papers and blogs by organizations ](https://github.com/eugeneyan/applied-ml)
2. [List Machine learning Python libraries](https://github.com/ml-tooling/best-of-ml-python)
3. [ML From Scratch - Implementations of models/algorithms](https://github.com/eriklindernoren/ML-From-Scratch)
4. [What the f*ck Python?](https://github.com/satwikkansal/wtfpython)
5. [scikit-learn user guide: step-step approach](https://scikit-learn.org/stable/user_guide.html)
6. [NLP Tutorial Code with DL](https://github.com/graykode/nlp-tutorial)
7. [awesome-mlops](https://github.com/visenger/awesome-mlops)
8. [Text Classification Algorithms: A Survey](https://github.com/kk7nc/Text_Classification)
9. [ML use cases by company](https://github.com/khangich/machine-learning-interview/blob/master/appliedml.md)
## Blogs and Webinar
1. [Recommendation algorithms and System design](https://www.theinsaneapp.com/2021/03/system-design-and-recommendation-algorithms.html)
2. [Machine Learning System Design](https://becominghuman.ai/machine-learning-system-design-f2f4018f2f8?gi=942874b21d0e)
3. [Lil'BLog](https://lilianweng.github.io/lil-log/)
## Must Read Research Paper
**NLP [Text]**
1. [Text Classification Algorithms: A Survey](https://arxiv.org/abs/1904.08067)
2. [Deep Learning Based Text Classification: A Comprehensive Review](https://arxiv.org/abs/2004.03705)
3. [Compression of Deep Learning Models for Text: A Survey](https://arxiv.org/abs/2008.05221)
4. [A Survey on Text Classification: From Shallow to Deep Learning](https://arxiv.org/pdf/2008.00364.pdf)
4. [A Survey of Transformers](https://arxiv.org/abs/2106.04554)
5. [AMMUS : A Survey of Transformer-based Pretrained Models in Natural Language Processing](https://arxiv.org/abs/2108.05542)
6. [Graph Neural Networks for Natural Language Processing: A Survey](https://arxiv.org/abs/2106.06090)
8. [A Survey of Data Augmentation Approaches for NLP](https://arxiv.org/abs/2105.03075)
9. [A Survey on Recent Approaches for Natural Language Processing in Low-Resource Scenarios](https://aclanthology.org/2021.naacl-main.201.pdf)
10. [Evaluation of Text Generation: A Survey](https://arxiv.org/pdf/2006.14799.pdf)
11. [A Survey of Transfer learning In NLP](https://arxiv.org/pdf/2007.04239.pdf)
12. [A Systematic Survey of Prompting Methods in NLP](https://arxiv.org/pdf/2107.13586.pdf)
**OCR [Optical Character Recognition]**
1. [Survey of Post-OCR Processing Approaches](https://dl.acm.org/doi/pdf/10.1145/3453476)
## Company Tech Blogs
1. [AssemblyAI](https://www.assemblyai.com/blog)
2. [Grammarly](https://www.grammarly.com/blog/engineering/)
3. [Huggingface](https://huggingface.co/blog)
4. [Uber](https://eng.uber.com/category/articles/ai/)
5. [Netflix](https://netflixtechblog.com/)
6. [Spotify Research](https://research.atspotify.com/blog/) | [Engineering](https://engineering.atspotify.com/)